The present invention is related to techniques and mechanisms for ranking a plurality of web objects, such as web documents or advertisements.
In recent years, the Internet has been a main source of information for millions of users. These users rely on the Internet to search for information of interest to them. One conventional way for users to search for information is to initiate a search term query through a search service's web page. Typically, a user can enter one or more search term(s) into an input box on the search web page and then initiate a search based on such entered search term(s). In response to a search term query, a ranking engine generally returns a ranked list of search result documents. In another ranking application, a query may pertain to a particular web document and a ranking engine is operable to locate a ranked list of advertisement links or pages that are relevant for such queried web document.
In most web page and advertisement ranking systems, there are a large number of parameters which have a significant impact on the objective metrics that are used by the ranking engine. Typically the parameters are tuned manually by domain experts and tune-and-test experiments. The overhead is very high and the time needed for the numerous manual tuning iterations is very long although optimal parameters are not always found. Moreover, the objective metrics to be optimized are typically changed in different use cases and scenarios.
Accordingly, it would be beneficial to provide improved mechanisms for tuning parameters for ranking mechanisms.